Awards: Premio Alumni SSC per la migliore tesi di Diploma (area scientifica), 2014Dissertation note: Tesi di diploma di 2° livello per la Classe delle Scienze Sperimentali Diploma di 2° livello Scuola Superiore di Catania, Catania, Italy 2014 A.A. 2012/2013 Abstract: Financial correlations play a key role in a wide number of relevant quantitative finance topics, such as option pricing, asset allocation and risk management. In this thesis we discuss some methods recently developed in the Econophysics literature to quantitatively investigate the properties of correlation matrices in financial markets. In particular, we focus on Random Matrix Theory and Correlation Based Graphs. Both methods are seen as filtering procedures aimed at detecting statistically reliable properties out of the unavoidable random component generated by measurement noise. We also discuss some possible applications of these techniques to asset allocation and option pricing. We finally perform a detailed study of the dynamics of financial correlations in the US equity market by using 49 industry index time series computed by K. French and E. Fama in the time period from July 1969 to December 2011. The main result of our analysis is the discovering of the presence of both a fast and a slow underlying dynamics for correlations along with significant distortions of the correlation structure in periods of market turmoil. Two major examples are the 1999-2001 dot-com bubble and the 2008-2009 financial crisis.Review: La tesi approfondisce con metodo e spirito critico opportune metodologie matematiche atte alla correlazione di serie temporali fornendo un esempio di applicazione ai mercati finanziari americani.

Financial correlations play a key role in a wide number of relevant quantitative finance topics, such as option pricing, asset allocation and risk management. In this thesis we discuss some methods recently developed in the Econophysics literature to quantitatively investigate the properties of correlation matrices in financial markets. In particular, we focus on Random Matrix Theory and Correlation Based Graphs. Both methods are seen as filtering procedures aimed at detecting statistically reliable properties out of the unavoidable random component generated by measurement noise. We also discuss some possible applications of these techniques to asset allocation and option pricing. We finally perform a detailed study of the dynamics of financial correlations in the US equity market by using 49 industry index time series computed by K. French and E. Fama in the time period from July 1969 to December 2011. The main result of our analysis is the discovering of the presence of both a fast and a slow underlying dynamics for correlations along with significant distortions of the correlation structure in periods of market turmoil. Two major examples are the 1999-2001 dot-com bubble and the 2008-2009 financial crisis.